def test_encode(self):
path = EnvironmentSettings.tmp_test_path + "abundance_encoder/"
PathBuilder.build(path)
repertoires, metadata = RepertoireBuilder.build([["GGG", "III", "LLL", "MMM"],
["DDD", "EEE", "FFF", "III", "LLL", "MMM"],
["CCC", "FFF", "MMM"],
["AAA", "CCC", "EEE", "FFF", "LLL", "MMM"]],
labels={"l1": [True, True, False, False]}, path=path)
dataset = RepertoireDataset(repertoires=repertoires, metadata_file=metadata, identifier="1")
encoder = SequenceAbundanceEncoder.build_object(dataset, **{
"comparison_attributes": ["sequence_aas"],
"p_value_threshold": 0.4, "sequence_batch_size": 4, "repertoire_batch_size": 8
})
label_config = LabelConfiguration([Label("l1", [True, False], positive_class=True)])
encoded_dataset = encoder.encode(dataset, EncoderParams(result_path=path, label_config=label_config))
self.assertTrue(np.array_equal(np.array([[1, 4], [1, 6], [0, 3], [0, 6]]), encoded_dataset.encoded_data.examples))
encoder.p_value_threshold = 0.05
encoded_dataset = encoder.encode(dataset, EncoderParams(result_path=path, label_config=label_config))
self.assertTrue(np.array_equal(np.array([[0, 4], [0, 6], [0, 3], [0, 6]]), encoded_dataset.encoded_data.examples))
shutil.rmtree(path)
def test_encode_sequence(self):
seq = ReceptorSequence(amino_acid_sequence="CASSVFRTY")
result = KmerSequenceEncoder.encode_sequence(
seq,
EncoderParams(model={"k": 3},
label_config=LabelConfiguration(),
result_path="",
pool_size=4))
self.assertTrue("CAS" in result)
self.assertTrue("ASS" in result)
self.assertTrue("SSV" in result)
self.assertTrue("SVF" in result)
self.assertTrue("VFR" in result)
self.assertTrue("FRT" in result)
self.assertTrue("RTY" in result)
self.assertEqual(7, len(result))
self.assertEqual(
KmerSequenceEncoder.encode_sequence(
ReceptorSequence(amino_acid_sequence="AC"),
EncoderParams(model={"k": 3},
label_config=LabelConfiguration(),
result_path="",
pool_size=4)), None)
def test_encode_sequence(self):
sequence = ReceptorSequence(
amino_acid_sequence="AAA",
metadata=SequenceMetadata(frame_type="OUT"))
enc = IdentitySequenceEncoder()
self.assertEqual(
enc.encode_sequence(
sequence,
EncoderParams(model={},
label_config=LabelConfiguration(),
result_path="")), ["AAA"])
sequence = ReceptorSequence(
amino_acid_sequence="AAA",
metadata=SequenceMetadata(frame_type="STOP"))
enc = IdentitySequenceEncoder()
self.assertEqual(
enc.encode_sequence(
sequence,
EncoderParams(model={},
label_config=LabelConfiguration(),
result_path="")), ["AAA"])
sequence = ReceptorSequence(amino_acid_sequence="AAA",
metadata=SequenceMetadata(frame_type="IN"))
enc = IdentitySequenceEncoder()
self.assertEqual(["AAA"],
enc.encode_sequence(
sequence,
EncoderParams(model={},
label_config=LabelConfiguration(),
result_path="")))
def encode_dataset(dataset,
hp_setting: HPSetting,
path: str,
learn_model: bool,
context: dict,
number_of_processes: int,
label_configuration: LabelConfiguration,
encode_labels: bool = True,
store_encoded_data: bool = False):
PathBuilder.build(path)
encoded_dataset = DataEncoder.run(
DataEncoderParams(dataset=dataset,
encoder=hp_setting.encoder,
encoder_params=EncoderParams(
model=hp_setting.encoder_params,
result_path=path,
pool_size=number_of_processes,
label_config=label_configuration,
learn_model=learn_model,
filename="train_dataset.pkl"
if learn_model else "test_dataset.pkl",
encode_labels=encode_labels),
store_encoded_data=store_encoded_data))
return encoded_dataset
def test_generate(self):
path = PathBuilder.build(f"{EnvironmentSettings.tmp_test_path}kernel_sequence_logo/")
dataset = RandomDatasetGenerator.generate_receptor_dataset(receptor_count=500, chain_1_length_probabilities={4: 1},
chain_2_length_probabilities={4: 1},
labels={"CMV": {True: 0.5, False: 0.5}}, path=path + "dataset/")
enc_dataset = OneHotReceptorEncoder(True, 1, False, "enc1").encode(dataset, EncoderParams(path + "result/",
LabelConfiguration([Label("CMV", [True, False])])))
cnn = ReceptorCNN(kernel_count=2, kernel_size=[3], positional_channels=3, sequence_type="amino_acid", device="cpu",
number_of_threads=4, random_seed=1, learning_rate=0.01, iteration_count=10, l1_weight_decay=0.1, evaluate_at=5,
batch_size=100, training_percentage=0.8, l2_weight_decay=0.0)
cnn.fit(enc_dataset.encoded_data, "CMV")
report = KernelSequenceLogo(method=cnn, result_path=path + "logos/")
report.generate_report()
self.assertTrue(os.path.isfile(f"{path}logos/alpha_kernel_3_1.png"))
self.assertTrue(os.path.isfile(f"{path}logos/alpha_kernel_3_2.png"))
self.assertTrue(os.path.isfile(f"{path}logos/beta_kernel_3_1.png"))
self.assertTrue(os.path.isfile(f"{path}logos/beta_kernel_3_2.png"))
self.assertTrue(os.path.isfile(f"{path}logos/alpha_kernel_3_1.csv"))
self.assertTrue(os.path.isfile(f"{path}logos/alpha_kernel_3_2.csv"))
self.assertTrue(os.path.isfile(f"{path}logos/beta_kernel_3_1.csv"))
self.assertTrue(os.path.isfile(f"{path}logos/beta_kernel_3_2.csv"))
self.assertTrue(os.path.isfile(f"{path}logos/fully_connected_layer_weights.csv"))
self.assertTrue(os.path.isfile(f"{path}logos/fully_connected_layer_weights.html"))
shutil.rmtree(path)
def test_encode_no_v(self):
path = EnvironmentSettings.root_path + "test/tmp/regex_matches_encoder/"
dataset, label_config, motif_filepath, labels = self.create_dummy_data(
path)
encoder = MatchedRegexEncoder.build_object(
dataset, **{
"motif_filepath": motif_filepath,
"match_v_genes": False,
"sum_counts": True
})
encoded = encoder.encode(
dataset,
EncoderParams(result_path=path,
label_config=label_config,
filename="dataset.csv"))
expected_outcome = [[20, 10, 0, 0], [0, 0, 10, 0], [0, 0, 0, 5]]
for index, row in enumerate(expected_outcome):
self.assertListEqual(list(encoded.encoded_data.examples[index]),
expected_outcome[index])
self.assertListEqual(["1_IGL", "1_IGH", "2_IGH", "3_IGL"],
encoded.encoded_data.feature_names)
self.assertListEqual(["subject_1", "subject_2", "subject_3"],
encoded.encoded_data.example_ids)
shutil.rmtree(path)
def test_sequence_flattened(self):
path = EnvironmentSettings.root_path + "test/tmp/onehot_seq_flat/"
PathBuilder.build(path)
dataset = self.construct_test_flatten_dataset(path)
encoder = OneHotEncoder.build_object(dataset, **{"use_positional_info": False, "distance_to_seq_middle": None, "flatten": True})
encoded_data = encoder.encode(dataset, EncoderParams(
result_path=path,
label_config=LabelConfiguration([Label(name="l1", values=[1, 0], positive_class="1")]),
pool_size=1,
learn_model=True,
model={},
filename="dataset.pkl"
))
self.assertTrue(isinstance(encoded_data, SequenceDataset))
onehot_a = [1.0] + [0.0] * 19
onehot_t = [0.0] * 16 + [1.0] + [0] * 3
self.assertListEqual(list(encoded_data.encoded_data.examples[0]), onehot_a+onehot_a+onehot_a+onehot_t+onehot_t+onehot_t)
self.assertListEqual(list(encoded_data.encoded_data.examples[1]), onehot_a+onehot_t+onehot_a+onehot_t+onehot_a+onehot_t)
self.assertListEqual(list(encoded_data.encoded_data.feature_names), [f"{pos}_{char}" for pos in range(6) for char in EnvironmentSettings.get_sequence_alphabet()])
shutil.rmtree(path)
def test(self):
path = EnvironmentSettings.tmp_test_path + "onehot_sequence/"
PathBuilder.build(path)
dataset, lc = self._construct_test_dataset(path)
encoder = OneHotEncoder.build_object(dataset, **{"use_positional_info": False,
"distance_to_seq_middle": None,
"flatten": False})
encoded_data = encoder.encode(dataset, EncoderParams(
result_path=f"{path}encoded/",
label_config=lc,
learn_model=True,
model={},
filename="dataset.pkl"
))
self.assertTrue(isinstance(encoded_data, SequenceDataset))
onehot_a = [1] + [0] * 19
onehot_t = [0] * 16 + [1] + [0] * 3
onehot_empty = [0] * 20
self.assertListEqual([list(item) for item in encoded_data.encoded_data.examples[0]], [onehot_a for i in range(4)])
self.assertListEqual([list(item) for item in encoded_data.encoded_data.examples[1]], [onehot_a, onehot_t, onehot_a, onehot_empty])
self.assertListEqual([list(item) for item in encoded_data.encoded_data.examples[2]], [onehot_a, onehot_t, onehot_t, onehot_empty])
self.assertListEqual(encoded_data.encoded_data.example_ids, [receptor.identifier for receptor in dataset.get_data()])
self.assertDictEqual(encoded_data.encoded_data.labels,
{"l1": [receptor_seq.get_attribute("l1") for receptor_seq in dataset.get_data()],
"l2": [receptor_seq.get_attribute("l2") for receptor_seq in dataset.get_data()]})
shutil.rmtree(path)
def get_encoded_repertoire(self, repertoire, params: EncoderParams):
params.model = vars(self)
return CacheHandler.memo_by_params((("encoding_model", params.model),
("labels", params.label_config.get_labels_by_name()),
("repertoire_id", repertoire.identifier),
("repertoire_data", hashlib.sha256(np.ascontiguousarray(repertoire.get_sequence_aas())).hexdigest())),
lambda: self.encode_repertoire(repertoire, params), CacheObjectType.ENCODING_STEP)
def test_run(self):
path = EnvironmentSettings.tmp_test_path + "mlapplicationtest/"
PathBuilder.build(path)
dataset = RandomDatasetGenerator.generate_repertoire_dataset(
50, {5: 1}, {5: 1}, {"l1": {
1: 0.5,
2: 0.5
}}, path + 'dataset/')
ml_method = LogisticRegression()
encoder = KmerFreqRepertoireEncoder(
NormalizationType.RELATIVE_FREQUENCY,
ReadsType.UNIQUE,
SequenceEncodingType.CONTINUOUS_KMER,
3,
scale_to_zero_mean=True,
scale_to_unit_variance=True)
label_config = LabelConfiguration([Label("l1", [1, 2])])
enc_dataset = encoder.encode(
dataset,
EncoderParams(result_path=path,
label_config=label_config,
filename="tmp_enc_dataset.pickle",
pool_size=4))
ml_method.fit(enc_dataset.encoded_data, 'l1')
hp_setting = HPSetting(
encoder, {
"normalization_type": "relative_frequency",
"reads": "unique",
"sequence_encoding": "continuous_kmer",
"k": 3,
"scale_to_zero_mean": True,
"scale_to_unit_variance": True
}, ml_method, {}, [], 'enc1', 'ml1')
PathBuilder.build(path + 'result/instr1/')
shutil.copy(path + 'dict_vectorizer.pickle',
path + 'result/instr1/dict_vectorizer.pickle')
shutil.copy(path + 'scaler.pickle',
path + 'result/instr1/scaler.pickle')
ml_app = MLApplicationInstruction(dataset, label_config, hp_setting, 4,
"instr1", False)
ml_app.run(path + 'result/')
predictions_path = path + "result/instr1/predictions.csv"
self.assertTrue(os.path.isfile(predictions_path))
df = pd.read_csv(predictions_path)
self.assertEqual(50, df.shape[0])
shutil.rmtree(path)
def _encode_sequence(self, sequence: ReceptorSequence,
params: EncoderParams, sequence_encoder, counts):
params.model = vars(self)
features = sequence_encoder.encode_sequence(sequence, params)
if features is not None:
for i in features:
if self.reads == ReadsType.UNIQUE:
counts[i] += 1
elif self.reads == ReadsType.ALL:
counts[i] += sequence.metadata.count
return counts
def test_encode(self):
path = PathBuilder.build(EnvironmentSettings.tmp_test_path + "atchley_kmer_encoding/")
dataset = RandomDatasetGenerator.generate_repertoire_dataset(3, {1: 1}, {4: 1}, {"l1": {True: 0.4, False: 0.6}}, path + "dataset/")
encoder = AtchleyKmerEncoder.build_object(dataset, **{"k": 2, "skip_first_n_aa": 1, "skip_last_n_aa": 1, "abundance": "RELATIVE_ABUNDANCE",
"normalize_all_features": False})
encoded_dataset = encoder.encode(dataset, EncoderParams(path + "result/", LabelConfiguration(labels=[Label("l1")])))
self.assertEqual((3, 11, 3), encoded_dataset.encoded_data.examples.shape)
self.assertEqual(0., encoded_dataset.encoded_data.examples[0, -1, 0])
shutil.rmtree(path)
def test_encode_sequence(self):
sequence = ReceptorSequence("ABCDEFG", None, None)
result = GappedKmerSequenceEncoder.encode_sequence(sequence, EncoderParams(model={"k_left": 3, "max_gap": 1},
label_config=LabelConfiguration(),
result_path=""))
self.assertEqual({'ABC.EFG', 'ABCDEF', 'BCDEFG'}, set(result))
result = GappedKmerSequenceEncoder.get_feature_names(EncoderParams(model={"k_left": 3, "max_gap": 1},
label_config=LabelConfiguration(),
result_path=""))
self.assertEqual({'sequence'}, set(result))
self.assertEqual(GappedKmerSequenceEncoder.encode_sequence(sequence, EncoderParams(model={"k_left": 10, "max_gap": 1},
label_config=LabelConfiguration(),
result_path="")),
None)
sequence.amino_acid_sequence = "ABCDEFG"
result = GappedKmerSequenceEncoder.encode_sequence(sequence, EncoderParams(model={"k_left": 3, "max_gap": 1},
label_config=LabelConfiguration(),
result_path=""))
self.assertEqual({'ABC.EFG', 'ABCDEF', 'BCDEFG'}, set(result))
result = GappedKmerSequenceEncoder.get_feature_names(EncoderParams(model={"k_left": 3, "max_gap": 1},
label_config=LabelConfiguration(),
result_path=""))
self.assertEqual({'sequence'}, set(result))
self.assertEqual(GappedKmerSequenceEncoder.encode_sequence(sequence, EncoderParams(model={"k_left": 10, "max_gap": 1},
label_config=LabelConfiguration(),
result_path="")),
None)
sequence.amino_acid_sequence = "ABCDEFG"
result = GappedKmerSequenceEncoder.encode_sequence(sequence,
EncoderParams(model={"k_left": 2,
"max_gap": 1,
"min_gap": 1,
"k_right": 3},
label_config=LabelConfiguration(),
result_path=""))
self.assertEqual({'AB.DEF', 'BC.EFG'}, set(result))
result = GappedKmerSequenceEncoder.get_feature_names(EncoderParams(model={"k_left": 2,
"max_gap": 1,
"min_gap": 1,
"k_right": 3},
label_config=LabelConfiguration(),
result_path=""))
self.assertEqual({'sequence'}, set(result))
def test_encode_sequence(self):
sequence = ReceptorSequence("CASSPRERATYEQCASSPRERATYEQCASSPRERATYEQ", None, None)
result = IMGTKmerSequenceEncoder.encode_sequence(sequence, EncoderParams(
model={"k": 3},
label_config=LabelConfiguration(),
result_path=""))
self.assertEqual({'CAS///105', 'ASS///106', 'SSP///107', 'SPR///108', 'PRE///109', 'RER///110', 'ERA///111',
'RAT///111.001', 'ATY///111.002', 'TYE///111.003', 'YEQ///111.004', 'EQC///111.005',
'QCA///111.006', 'CAS///111.007', 'ASS///111.008', 'SSP///111.009', 'SPR///111.01',
'PRE///111.011', 'RER///111.012', 'ERA///111.013', 'RAT///112.013', 'ATY///112.012',
'TYE///112.011', 'YEQ///112.01', 'EQC///112.009', 'QCA///112.008', 'CAS///112.007',
'ASS///112.006', 'SSP///112.005', 'SPR///112.004', 'PRE///112.003', 'RER///112.002',
'ERA///112.001', 'RAT///112', 'ATY///113', 'TYE///114', 'YEQ///115'},
set(result))
self.assertEqual(len(result), len(sequence.get_sequence()) - 3 + 1)
sequence = ReceptorSequence("AHCDE", None, None)
result = IMGTKmerSequenceEncoder.encode_sequence(sequence, EncoderParams(
model={"k": 3},
label_config=LabelConfiguration(),
result_path=""))
self.assertEqual({'AHC///105', 'HCD///106', 'CDE///107'},
set(result))
self.assertEqual(len(result), len(sequence.get_sequence()) - 3 + 1)
self.assertEqual(
IMGTKmerSequenceEncoder.encode_sequence(
sequence,
EncoderParams(model={"k": 25},
label_config=LabelConfiguration(),
result_path="")
),
None
)
def test_encode_sequence(self):
sequence = ReceptorSequence("AHCDE", None, None)
kmers = IMGTGappedKmerEncoder.encode_sequence(
sequence,
EncoderParams(model={
"k_left": 1,
"max_gap": 1
},
label_config=LabelConfiguration(),
result_path=""))
self.assertEqual(
{
'AH///105', 'HC///106', 'CD///107', 'DE///116', 'A.C///105',
'H.D///106', 'C.E///107'
}, set(kmers))
sequence = ReceptorSequence("CASSPRERATYEQCAY", None, None)
kmers = IMGTGappedKmerEncoder.encode_sequence(
sequence,
EncoderParams(model={
"k_left": 1,
"max_gap": 1
},
label_config=LabelConfiguration(),
result_path=""))
self.assertEqual(
{
'CA///105', 'AS///106', 'SS///107', 'SP///108', 'PR///109',
'RE///110', 'ER///111', 'RA///111.001', 'AT///112.002',
'TY///112.001', 'YE///112', 'EQ///113', 'QC///114', 'CA///115',
'AY///116', 'C.S///105', 'A.S///106', 'S.P///107', 'S.R///108',
'P.E///109', 'R.R///110', 'E.A///111', 'R.T///111.001',
'A.Y///112.002', 'T.E///112.001', 'Y.Q///112', 'E.C///113',
'Q.A///114', 'C.Y///115'
}, set(kmers))
def test_generate(self):
path = PathBuilder.build(EnvironmentSettings.tmp_test_path +
"tcrdist_motif_discovery/")
dataset_path = self._create_dataset(path)
dataset = SingleLineReceptorImport.import_dataset(
{
"path":
dataset_path,
"result_path":
path + "dataset/",
"separator":
",",
"columns_to_load": [
"subject", "epitope", "count", "v_a_gene", "j_a_gene",
"cdr3_a_aa", "v_b_gene", "j_b_gene", "cdr3_b_aa",
"clone_id", "cdr3_a_nucseq", "cdr3_b_nucseq"
],
"column_mapping": {
"cdr3_a_aa": "alpha_amino_acid_sequence",
"cdr3_b_aa": "beta_amino_acid_sequence",
"cdr3_a_nucseq": "alpha_nucleotide_sequence",
"cdr3_b_nucseq": "beta_nucleotide_sequence",
"v_a_gene": "alpha_v_gene",
"v_b_gene": "beta_v_gene",
"j_a_gene": "alpha_j_gene",
"j_b_gene": "beta_j_gene",
"clone_id": "identifier"
},
"receptor_chains":
"TRA_TRB",
"region_type":
"IMGT_CDR3",
"sequence_file_size":
50000,
"organism":
"mouse"
}, 'd1')
dataset = TCRdistEncoder(8).encode(
dataset,
EncoderParams(f"{path}result/",
LabelConfiguration([Label("epitope")])))
report = TCRdistMotifDiscovery(dataset, path + "report/",
"report name", 8)
report.generate_report()
shutil.rmtree(path)
def test__encode_new_dataset(self):
path = EnvironmentSettings.root_path + "test/tmp/matched_receptors_encoder/"
dataset, label_config, reference_sequences, labels = self.create_dummy_data(
path)
encoder = MatchedSequencesEncoder.build_object(
dataset, **{
"reference": reference_sequences,
"max_edit_distance": 0
})
encoded = encoder.encode(
dataset,
EncoderParams(result_path=path,
label_config=label_config,
filename="dataset.csv"))
expected_outcome = [[10, 0], [0, 10], [0, 5]]
for index, row in enumerate(expected_outcome):
self.assertListEqual(list(encoded.encoded_data.examples[index]),
expected_outcome[index])
self.assertDictEqual(
encoded.encoded_data.labels, {
"label": ["yes", "yes", "no"],
"subject_id": ["subject_1", "subject_2", "subject_3"]
})
self.assertListEqual(encoded.encoded_data.feature_names,
["100_TRB", "200_TRB"])
self.assertListEqual(
list(encoded.encoded_data.feature_annotations.sequence_id),
["100_TRB", "200_TRB"])
self.assertListEqual(
list(encoded.encoded_data.feature_annotations.chain),
["beta", "beta"])
self.assertListEqual(
list(encoded.encoded_data.feature_annotations.sequence),
["AAAA", "SSSS"])
self.assertListEqual(
list(encoded.encoded_data.feature_annotations.v_gene),
["TRBV1", "TRBV1"])
self.assertListEqual(
list(encoded.encoded_data.feature_annotations.j_gene),
["TRBJ1", "TRBJ1"])
shutil.rmtree(path)
def test_run(self):
path = EnvironmentSettings.root_path + "test/tmp/dataencoder/"
PathBuilder.build(path)
rep1 = Repertoire.build_from_sequence_objects(
[ReceptorSequence("AAA", identifier="1")],
metadata={
"l1": 1,
"l2": 2
},
path=path)
rep2 = Repertoire.build_from_sequence_objects(
[ReceptorSequence("ATA", identifier="2")],
metadata={
"l1": 0,
"l2": 3
},
path=path)
lc = LabelConfiguration()
lc.add_label("l1", [1, 2])
lc.add_label("l2", [0, 3])
dataset = RepertoireDataset(repertoires=[rep1, rep2])
encoder = Word2VecEncoder.build_object(
dataset, **{
"k": 3,
"model_type": ModelType.SEQUENCE.name,
"vector_size": 6
})
res = DataEncoder.run(
DataEncoderParams(dataset=dataset,
encoder=encoder,
encoder_params=EncoderParams(
model={},
pool_size=2,
label_config=lc,
result_path=path,
filename="dataset.csv"),
store_encoded_data=False))
self.assertTrue(isinstance(res, RepertoireDataset))
self.assertTrue(res.encoded_data.examples.shape[0] == 2)
shutil.rmtree(path)
def test_repertoire_flattened(self):
path = EnvironmentSettings.root_path + "test/tmp/onehot_recep_flat/"
PathBuilder.build(path)
dataset, lc = self._construct_test_repertoiredataset(path,
positional=False)
encoder = OneHotEncoder.build_object(
dataset, **{
"use_positional_info": False,
"distance_to_seq_middle": None,
"flatten": True
})
encoded_data = encoder.encode(
dataset,
EncoderParams(result_path=path,
label_config=lc,
pool_size=1,
learn_model=True,
model={},
filename="dataset.pkl"))
self.assertTrue(isinstance(encoded_data, RepertoireDataset))
onehot_a = [1.0] + [0.0] * 19
onehot_t = [0.0] * 16 + [1.0] + [0] * 3
onehot_empty = [0] * 20
self.assertListEqual(
list(encoded_data.encoded_data.examples[0]), onehot_a + onehot_a +
onehot_a + onehot_a + onehot_a + onehot_t + onehot_a +
onehot_empty + onehot_a + onehot_t + onehot_a + onehot_empty)
self.assertListEqual(
list(encoded_data.encoded_data.examples[1]),
onehot_a + onehot_t + onehot_a + onehot_empty + onehot_t +
onehot_a + onehot_a + onehot_empty + onehot_empty + onehot_empty +
onehot_empty + onehot_empty)
self.assertListEqual(list(encoded_data.encoded_data.feature_names), [
f"{seq}_{pos}_{char}" for seq in range(3) for pos in range(4)
for char in EnvironmentSettings.get_sequence_alphabet()
])
shutil.rmtree(path)
def test_encode(self):
test_path = EnvironmentSettings.root_path + "test/tmp/w2v/"
PathBuilder.build(test_path)
sequence1 = ReceptorSequence("CASSVFA", identifier="1")
sequence2 = ReceptorSequence("CASSCCC", identifier="2")
metadata1 = {"T1D": "T1D", "subject_id": "1"}
rep1 = Repertoire.build_from_sequence_objects([sequence1, sequence2],
test_path, metadata1)
metadata2 = {"T1D": "CTL", "subject_id": "2"}
rep2 = Repertoire.build_from_sequence_objects([sequence1], test_path,
metadata2)
dataset = RepertoireDataset(repertoires=[rep1, rep2])
label_configuration = LabelConfiguration()
label_configuration.add_label("T1D", ["T1D", "CTL"])
config_params = EncoderParams(model={},
learn_model=True,
result_path=test_path,
label_config=label_configuration,
filename="dataset.pkl")
encoder = Word2VecEncoder.build_object(
dataset, **{
"k": 3,
"model_type": "sequence",
"vector_size": 16
})
encoded_dataset = encoder.encode(dataset=dataset, params=config_params)
self.assertIsNotNone(encoded_dataset.encoded_data)
self.assertTrue(encoded_dataset.encoded_data.examples.shape[0] == 2)
self.assertTrue(encoded_dataset.encoded_data.examples.shape[1] == 16)
self.assertTrue(len(encoded_dataset.encoded_data.labels["T1D"]) == 2)
self.assertTrue(encoded_dataset.encoded_data.labels["T1D"][0] == "T1D")
self.assertTrue(isinstance(encoder, W2VRepertoireEncoder))
shutil.rmtree(test_path)
def encode_dataset_by_kmer_freq(path_to_dataset_directory: str, result_path: str, metadata_path: str = None):
"""
encodes the repertoire dataset using KmerFrequencyEncoder
:param path_to_dataset_directory: path to directory containing all repertoire files with .tsv extension in MiXCR format
:param result_path: where to store the results
:param metadata_path: csv file with columns "filename", "subject_id", "disease" which is filled by default if value of argument is None,
otherwise any metadata csv file passed to the function, must include filename and subject_id columns,
and an arbitrary disease column
:return: encoded dataset with encoded data in encoded_dataset.encoded_data.examples
"""
if metadata_path is None:
metadata_path = generate_random_metadata(path_to_dataset_directory, result_path)
loader = MiXCRImport()
dataset = loader.import_dataset({
"is_repertoire": True,
"path": path_to_dataset_directory,
"metadata_file": metadata_path,
"region_type": "IMGT_CDR3", # import_dataset in only cdr3
"number_of_processes": 4, # number of parallel processes for loading the data
"result_path": result_path,
"separator": "\t",
"columns_to_load": ["cloneCount", "allVHitsWithScore", "allJHitsWithScore", "aaSeqCDR3", "nSeqCDR3"],
"column_mapping": {
"cloneCount": "counts",
"allVHitsWithScore": "v_genes",
"allJHitsWithScore": "j_genes"
},
}, "mixcr_dataset")
label_name = list(dataset.params.keys())[0] # label that can be used for ML prediction - by default: "disease" with values True/False
encoded_dataset = DataEncoder.run(DataEncoderParams(dataset, KmerFrequencyEncoder.build_object(dataset, **{
"normalization_type": "relative_frequency", # encode repertoire by the relative frequency of k-mers in repertoire
"reads": "unique", # count each sequence only once, do not use clonal count
"k": 2, # k-mer length
"sequence_encoding": "continuous_kmer" # split each sequence in repertoire to overlapping k-mers
}), EncoderParams(result_path=result_path,
label_config=LabelConfiguration([Label(label_name, dataset.params[label_name])])), False))
dataset_exporter = DesignMatrixExporter(dataset=encoded_dataset,
result_path=f"{result_path if result_path[:-1] == '/' else result_path+'/'}csv_exported/")
dataset_exporter.generate_report()
return encoded_dataset
def test(self):
path = EnvironmentSettings.tmp_test_path + "onehot_sequence_1/"
PathBuilder.build(path)
dataset, lc = self._construct_test_dataset(path)
encoder = OneHotEncoder.build_object(
dataset, **{
"use_positional_info": False,
"distance_to_seq_middle": 6,
"flatten": False
})
encoded_data = encoder.encode(
dataset,
EncoderParams(result_path=f"{path}encoded/",
label_config=lc,
learn_model=True,
model={},
filename="dataset.pkl"))
self.assertTrue(isinstance(encoded_data, ReceptorDataset))
onehot_a = [1] + [0] * 19
onehot_t = [0] * 16 + [1] + [0] * 3
onehot_empty = [0] * 20
self.assertListEqual(
[list(item) for item in encoded_data.encoded_data.examples[0, 0]],
[onehot_a for i in range(4)])
self.assertListEqual(
[list(item) for item in encoded_data.encoded_data.examples[0, 1]],
[onehot_a, onehot_t, onehot_a, onehot_empty])
self.assertListEqual(
[list(item) for item in encoded_data.encoded_data.examples[1, 0]],
[onehot_a, onehot_t, onehot_a, onehot_empty])
self.assertListEqual(
[list(item) for item in encoded_data.encoded_data.examples[1, 1]],
[onehot_a, onehot_t, onehot_t, onehot_empty])
shutil.rmtree(path)
def test_encode(self):
file_content = """complex.id Gene CDR3 V J Species MHC A MHC B MHC class Epitope Epitope gene Epitope species Reference Method Meta CDR3fix Score
3050 TRB CASSPPRVYSNGAGLAGVGWRNEQFF TRBV5-4*01 TRBJ2-1*01 HomoSapiens HLA-A*11:01 B2M MHCI AVFDRKSDAK EBNA4 EBV https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/# {"frequency": "1/11684", "identification": "dextramer-sort", "sequencing": "rna-seq", "singlecell": "yes", "verification": ""} {"cell.subset": "", "clone.id": "", "donor.MHC": "", "donor.MHC.method": "", "epitope.id": "", "replica.id": "", "samples.found": 1, "structure.id": "", "studies.found": 1, "study.id": "", "subject.cohort": "", "subject.id": "1", "tissue": ""} {"cdr3": "CASSPPRVYSNGAGLAGVGWRNEQFF", "cdr3_old": "CASSPPRVYSNGAGLAGVGWRNEQFF", "fixNeeded": false, "good": true, "jCanonical": true, "jFixType": "NoFixNeeded", "jId": "TRBJ2-1*01", "jStart": 21, "vCanonical": true, "vEnd": 4, "vFixType": "NoFixNeeded", "vId": "TRBV5-4*01"} 0
15760 TRB CASSWTWDAATLWGQGALGGANVLTF TRBV5-5*01 TRBJ2-6*01 HomoSapiens HLA-A*03:01 B2M MHCI KLGGALQAK IE1 CMV https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/# {"frequency": "1/25584", "identification": "dextramer-sort", "sequencing": "rna-seq", "singlecell": "yes", "verification": ""} {"cell.subset": "", "clone.id": "", "donor.MHC": "", "donor.MHC.method": "", "epitope.id": "", "replica.id": "", "samples.found": 1, "structure.id": "", "studies.found": 1, "study.id": "", "subject.cohort": "", "subject.id": "3", "tissue": ""} {"cdr3": "CASSWTWDAATLWGQGALGGANVLTF", "cdr3_old": "CASSWTWDAATLWGQGALGGANVLTF", "fixNeeded": false, "good": true, "jCanonical": true, "jFixType": "NoFixNeeded", "jId": "TRBJ2-6*01", "jStart": 19, "vCanonical": true, "vEnd": 4, "vFixType": "NoFixNeeded", "vId": "TRBV5-5*01"} 0
3050 TRA CAAIYESRGSTLGRLYF TRAV13-1*01 TRAJ18*01 HomoSapiens HLA-A*11:01 B2M MHCI AVFDRKSDAK EBNA4 EBV https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/# {"frequency": "1/11684", "identification": "dextramer-sort", "sequencing": "rna-seq", "singlecell": "yes", "verification": ""} {"cell.subset": "", "clone.id": "", "donor.MHC": "", "donor.MHC.method": "", "epitope.id": "", "replica.id": "", "samples.found": 1, "structure.id": "", "studies.found": 1, "study.id": "", "subject.cohort": "", "subject.id": "1", "tissue": ""} {"cdr3": "CAAIYESRGSTLGRLYF", "cdr3_old": "CAAIYESRGSTLGRLYF", "fixNeeded": false, "good": true, "jCanonical": true, "jFixType": "NoFixNeeded", "jId": "TRAJ18*01", "jStart": 7, "oldVEnd": -1, "oldVFixType": "FailedBadSegment", "oldVId": null, "vCanonical": true, "vEnd": 3, "vFixType": "ChangeSegment", "vId": "TRAV13-1*01"} 0
15760 TRA CALRLNNQGGKLIF TRAV9-2*01 TRAJ23*01 HomoSapiens HLA-A*03:01 B2M MHCI KLGGALQAK IE1 CMV https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/# {"frequency": "1/25584", "identification": "dextramer-sort", "sequencing": "rna-seq", "singlecell": "yes", "verification": ""} {"cell.subset": "", "clone.id": "", "donor.MHC": "", "donor.MHC.method": "", "epitope.id": "", "replica.id": "", "samples.found": 1, "structure.id": "", "studies.found": 1, "study.id": "", "subject.cohort": "", "subject.id": "3", "tissue": ""} {"cdr3": "CALRLNNQGGKLIF", "cdr3_old": "CALRLNNQGGKLIF", "fixNeeded": false, "good": true, "jCanonical": true, "jFixType": "NoFixNeeded", "jId": "TRAJ23*01", "jStart": 6, "vCanonical": true, "vEnd": 3, "vFixType": "NoFixNeeded", "vId": "TRAV9-2*01"} 0
3051 TRB CASSPPRVYSNGAGLAGVGWRNEQFF TRBV5-4*01 TRBJ2-1*01 HomoSapiens HLA-A*11:01 B2M MHCI AVFDRKSDAK EBNA4 EBV https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/# {"frequency": "1/11684", "identification": "dextramer-sort", "sequencing": "rna-seq", "singlecell": "yes", "verification": ""} {"cell.subset": "", "clone.id": "", "donor.MHC": "", "donor.MHC.method": "", "epitope.id": "", "replica.id": "", "samples.found": 1, "structure.id": "", "studies.found": 1, "study.id": "", "subject.cohort": "", "subject.id": "1", "tissue": ""} {"cdr3": "CASSPPRVYSNGAGLAGVGWRNEQFF", "cdr3_old": "CASSPPRVYSNGAGLAGVGWRNEQFF", "fixNeeded": false, "good": true, "jCanonical": true, "jFixType": "NoFixNeeded", "jId": "TRBJ2-1*01", "jStart": 21, "vCanonical": true, "vEnd": 4, "vFixType": "NoFixNeeded", "vId": "TRBV5-4*01"} 0
15761 TRB CASSWTWDAATLWGQGALGGANVLTF TRBV5-5*01 TRBJ2-6*01 HomoSapiens HLA-A*03:01 B2M MHCI KLGGALQAK IE1 CMV https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/# {"frequency": "1/25584", "identification": "dextramer-sort", "sequencing": "rna-seq", "singlecell": "yes", "verification": ""} {"cell.subset": "", "clone.id": "", "donor.MHC": "", "donor.MHC.method": "", "epitope.id": "", "replica.id": "", "samples.found": 1, "structure.id": "", "studies.found": 1, "study.id": "", "subject.cohort": "", "subject.id": "3", "tissue": ""} {"cdr3": "CASSWTWDAATLWGQGALGGANVLTF", "cdr3_old": "CASSWTWDAATLWGQGALGGANVLTF", "fixNeeded": false, "good": true, "jCanonical": true, "jFixType": "NoFixNeeded", "jId": "TRBJ2-6*01", "jStart": 19, "vCanonical": true, "vEnd": 4, "vFixType": "NoFixNeeded", "vId": "TRBV5-5*01"} 0
3051 TRA CAAIYESRGSTLGRLYF TRAV13-1*01 TRAJ18*01 HomoSapiens HLA-A*11:01 B2M MHCI AVFDRKSDAK EBNA4 EBV https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/# {"frequency": "1/11684", "identification": "dextramer-sort", "sequencing": "rna-seq", "singlecell": "yes", "verification": ""} {"cell.subset": "", "clone.id": "", "donor.MHC": "", "donor.MHC.method": "", "epitope.id": "", "replica.id": "", "samples.found": 1, "structure.id": "", "studies.found": 1, "study.id": "", "subject.cohort": "", "subject.id": "1", "tissue": ""} {"cdr3": "CAAIYESRGSTLGRLYF", "cdr3_old": "CAAIYESRGSTLGRLYF", "fixNeeded": false, "good": true, "jCanonical": true, "jFixType": "NoFixNeeded", "jId": "TRAJ18*01", "jStart": 7, "oldVEnd": -1, "oldVFixType": "FailedBadSegment", "oldVId": null, "vCanonical": true, "vEnd": 3, "vFixType": "ChangeSegment", "vId": "TRAV13-1*01"} 0
15761 TRA CALRLNNQGGKLIF TRAV9-2*01 TRAJ23*01 HomoSapiens HLA-A*03:01 B2M MHCI KLGGALQAK IE1 CMV https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/# {"frequency": "1/25584", "identification": "dextramer-sort", "sequencing": "rna-seq", "singlecell": "yes", "verification": ""} {"cell.subset": "", "clone.id": "", "donor.MHC": "", "donor.MHC.method": "", "epitope.id": "", "replica.id": "", "samples.found": 1, "structure.id": "", "studies.found": 1, "study.id": "", "subject.cohort": "", "subject.id": "3", "tissue": ""} {"cdr3": "CALRLNNQGGKLIF", "cdr3_old": "CALRLNNQGGKLIF", "fixNeeded": false, "good": true, "jCanonical": true, "jFixType": "NoFixNeeded", "jId": "TRAJ23*01", "jStart": 6, "vCanonical": true, "vEnd": 3, "vFixType": "NoFixNeeded", "vId": "TRAV9-2*01"} 0
"""
path = PathBuilder.build(EnvironmentSettings.root_path +
"test/tmp/trcdist_encoder/")
with open(path + "receptors.tsv", "w") as file:
file.writelines(file_content)
params = DefaultParamsLoader.load(
EnvironmentSettings.default_params_path + "datasets/", "vdjdb")
params["is_repertoire"] = False
params["paired"] = True
params["result_path"] = path
params["path"] = path
params["sequence_file_size"] = 1
params["receptor_chains"] = "TRA_TRB"
params['organism'] = 'human'
dataset = VDJdbImport.import_dataset(params, "vdjdb_dataset")
encoder = TCRdistEncoder.build_object(dataset, **{"cores": 2})
encoded_dataset = encoder.encode(
dataset,
EncoderParams(f"{path}result/",
LabelConfiguration([Label("epitope")])))
self.assertTrue(encoded_dataset.encoded_data.examples.shape[0]
== encoded_dataset.encoded_data.examples.shape[1]
and encoded_dataset.encoded_data.examples.shape[0]
== dataset.get_example_count())
shutil.rmtree(path)
def test_encode(self):
path = EnvironmentSettings.tmp_test_path + "count_encoder/"
PathBuilder.build(path)
repertoires, metadata = RepertoireBuilder.build(
[["GGG", "III", "LLL", "MMM"],
["DDD", "EEE", "FFF", "III", "LLL", "MMM"], ["CCC", "FFF", "MMM"],
["AAA", "CCC", "EEE", "FFF", "LLL", "MMM"]],
labels={"l1": [True, True, False, False]},
path=path)
dataset = RepertoireDataset(repertoires=repertoires,
metadata_file=metadata,
identifier="1")
encoder = SequenceCountEncoder.build_object(
dataset, **{
"comparison_attributes": ["sequence_aas"],
"p_value_threshold": 0.4,
"sequence_batch_size": 4
})
label_config = LabelConfiguration(
[Label("l1", [True, False], positive_class=True)])
encoded_dataset = encoder.encode(
dataset, EncoderParams(result_path=path,
label_config=label_config))
test = encoded_dataset.encoded_data.examples
self.assertTrue(test[0] == 1)
self.assertTrue(test[1] == 1)
self.assertTrue(test[2] == 0)
self.assertTrue(test[3] == 0)
self.assertTrue("III" in encoded_dataset.encoded_data.feature_names)
shutil.rmtree(path)
def test_encode(self):
path = EnvironmentSettings.tmp_test_path + "distance_encoder/"
PathBuilder.build(path)
dataset = self.create_dataset(path)
enc = DistanceEncoder.build_object(
dataset, **{
"distance_metric": DistanceMetricType.JACCARD.name,
"attributes_to_match": ["sequence_aas"],
"sequence_batch_size": 20
})
enc.set_context({"dataset": dataset})
encoded = enc.encode(
dataset,
EncoderParams(result_path=path,
label_config=LabelConfiguration(
[Label("l1", [0, 1]),
Label("l2", [2, 3])]),
pool_size=4,
filename="dataset.pkl"))
self.assertEqual(8, encoded.encoded_data.examples.shape[0])
self.assertEqual(8, encoded.encoded_data.examples.shape[1])
self.assertEqual(1, encoded.encoded_data.examples.iloc[0, 0])
self.assertEqual(1, encoded.encoded_data.examples.iloc[1, 1])
self.assertEqual(1, encoded.encoded_data.examples.iloc[0, 4])
self.assertTrue(
np.array_equal([1, 0, 1, 0, 1, 0, 1, 0],
encoded.encoded_data.labels["l1"]))
self.assertTrue(
np.array_equal([2, 3, 2, 3, 2, 3, 3, 3],
encoded.encoded_data.labels["l2"]))
shutil.rmtree(path)
def test_encode(self):
path = EnvironmentSettings.tmp_test_path + "deeprc_encoder/"
PathBuilder.build(path)
PathBuilder.build(path + "encoded_data/")
main_dataset, sub_dataset = self.create_datasets(path)
enc = DeepRCEncoder.build_object(sub_dataset, **{})
enc.set_context({"dataset": main_dataset})
encoded = enc.encode(
sub_dataset,
EncoderParams(result_path=path + "encoded_data/",
label_config=LabelConfiguration(
[Label("l1", [0, 1]),
Label("l2", [2, 3])]),
pool_size=4))
self.assertListEqual(encoded.encoded_data.example_ids,
sub_dataset.get_repertoire_ids())
self.assertTrue(
os.path.isfile(encoded.encoded_data.info["metadata_filepath"]))
metadata_content = pd.read_csv(
encoded.encoded_data.info["metadata_filepath"], sep="\t")
self.assertListEqual(list(metadata_content["ID"]),
sub_dataset.get_repertoire_ids())
for repertoire in main_dataset.repertoires:
rep_path = f"{path}/encoded_data/encoding/{repertoire.identifier}.tsv"
self.assertTrue(os.path.isfile(rep_path))
repertoire_tsv = pd.read_csv(rep_path, sep="\t")
self.assertListEqual(list(repertoire_tsv["amino_acid"]),
list(repertoire.get_sequence_aas()))
shutil.rmtree(path)
def test_fit(self):
path = PathBuilder.build(EnvironmentSettings.tmp_test_path +
"kmermil/")
repertoire_count = 10
dataset = RandomDatasetGenerator.generate_repertoire_dataset(
repertoire_count=repertoire_count,
sequence_count_probabilities={2: 1},
sequence_length_probabilities={4: 1},
labels={"l1": {
True: 0.5,
False: 0.5
}},
path=path + "dataset/")
enc_dataset = AtchleyKmerEncoder(
2, 1, 1, 'relative_abundance', False).encode(
dataset,
EncoderParams(path + "result/",
LabelConfiguration([Label("l1",
[True, False])])))
cls = AtchleyKmerMILClassifier(iteration_count=10,
threshold=-0.0001,
evaluate_at=2,
use_early_stopping=False,
random_seed=1,
learning_rate=0.01,
zero_abundance_weight_init=True,
number_of_threads=8)
cls.fit(enc_dataset.encoded_data, "l1")
predictions = cls.predict(enc_dataset.encoded_data, "l1")
self.assertEqual(repertoire_count, len(predictions["l1"]))
self.assertEqual(
repertoire_count,
len([pred for pred in predictions["l1"]
if isinstance(pred, bool)]))
predictions_proba = cls.predict_proba(enc_dataset.encoded_data, "l1")
self.assertEqual(repertoire_count,
np.rint(np.sum(predictions_proba["l1"])))
self.assertEqual(repertoire_count, predictions_proba["l1"].shape[0])
cls.store(path + "model_storage/",
feature_names=enc_dataset.encoded_data.feature_names)
cls2 = AtchleyKmerMILClassifier(iteration_count=10,
threshold=-0.0001,
evaluate_at=2,
use_early_stopping=False,
random_seed=1,
learning_rate=0.01,
zero_abundance_weight_init=True,
number_of_threads=8)
cls2.load(path + "model_storage/")
cls2_vars = vars(cls2)
del cls2_vars["logistic_regression"]
cls_vars = vars(cls)
del cls_vars["logistic_regression"]
for item, value in cls_vars.items():
if not isinstance(value, np.ndarray):
loaded_value = cls2_vars[item]
self.assertEqual(value, loaded_value)
model = cls.get_model("l1")
self.assertEqual(vars(cls), model)
shutil.rmtree(path)
def test_fit(self):
path = PathBuilder.build(EnvironmentSettings.tmp_test_path + "cnn/")
dataset = RandomDatasetGenerator.generate_receptor_dataset(
receptor_count=500,
chain_1_length_probabilities={4: 1},
chain_2_length_probabilities={4: 1},
labels={"CMV": {
True: 0.5,
False: 0.5
}},
path=path + "dataset/")
enc_dataset = OneHotReceptorEncoder(True, 1, False, "enc1").encode(
dataset,
EncoderParams(path + "result/",
LabelConfiguration([Label("CMV", [True, False])])))
cnn = ReceptorCNN(kernel_count=2,
kernel_size=[3],
positional_channels=3,
sequence_type="amino_acid",
device="cpu",
number_of_threads=4,
random_seed=1,
learning_rate=0.01,
iteration_count=10,
l1_weight_decay=0.1,
evaluate_at=5,
batch_size=100,
training_percentage=0.8,
l2_weight_decay=0.0)
cnn.fit(encoded_data=enc_dataset.encoded_data, label_name="CMV")
predictions = cnn.predict(enc_dataset.encoded_data, "CMV")
self.assertEqual(500, len(predictions["CMV"]))
self.assertEqual(
500,
len([
pred for pred in predictions["CMV"] if isinstance(pred, bool)
]))
predictions_proba = cnn.predict_proba(enc_dataset.encoded_data, "CMV")
self.assertEqual(500, np.rint(np.sum(predictions_proba["CMV"])))
self.assertEqual(500, predictions_proba["CMV"].shape[0])
cnn.store(path + "model_storage/")
cnn2 = ReceptorCNN(sequence_type="amino_acid")
cnn2.load(path + "model_storage/")
cnn2_vars = vars(cnn2)
del cnn2_vars["CNN"]
cnn_vars = vars(cnn)
del cnn_vars["CNN"]
for item, value in cnn_vars.items():
if not isinstance(value, np.ndarray):
self.assertEqual(value, cnn2_vars[item])
model = cnn.get_model(["CMV"])
self.assertEqual(vars(cnn), model)
shutil.rmtree(path)
def test_encode(self):
path = EnvironmentSettings.root_path + "test/tmp/kmerfreqenc/"
PathBuilder.build(path)
rep1 = Repertoire.build_from_sequence_objects([ReceptorSequence("AAA", identifier="1"),
ReceptorSequence("ATA", identifier="2"),
ReceptorSequence("ATA", identifier='3')],
metadata={"l1": 1, "l2": 2, "subject_id": "1"}, path=path)
rep2 = Repertoire.build_from_sequence_objects([ReceptorSequence("ATA", identifier="1"),
ReceptorSequence("TAA", identifier="2"),
ReceptorSequence("AAC", identifier="3")],
metadata={"l1": 0, "l2": 3, "subject_id": "2"}, path=path)
lc = LabelConfiguration()
lc.add_label("l1", [1, 2])
lc.add_label("l2", [0, 3])
dataset = RepertoireDataset(repertoires=[rep1, rep2])
encoder = KmerFrequencyEncoder.build_object(dataset, **{
"normalization_type": NormalizationType.RELATIVE_FREQUENCY.name,
"reads": ReadsType.UNIQUE.name,
"sequence_encoding": SequenceEncodingType.IDENTITY.name,
"k": 3
})
d1 = encoder.encode(dataset, EncoderParams(
result_path=path + "1/",
label_config=lc,
learn_model=True,
model={},
filename="dataset.pkl"
))
encoder = KmerFrequencyEncoder.build_object(dataset, **{
"normalization_type": NormalizationType.RELATIVE_FREQUENCY.name,
"reads": ReadsType.UNIQUE.name,
"sequence_encoding": SequenceEncodingType.CONTINUOUS_KMER.name,
"k": 3
})
d2 = encoder.encode(dataset, EncoderParams(
result_path=path + "2/",
label_config=lc,
pool_size=2,
learn_model=True,
model={},
filename="dataset.csv"
))
encoder3 = KmerFrequencyEncoder.build_object(dataset, **{
"normalization_type": NormalizationType.BINARY.name,
"reads": ReadsType.UNIQUE.name,
"sequence_encoding": SequenceEncodingType.CONTINUOUS_KMER.name,
"k": 3
})
d3 = encoder3.encode(dataset, EncoderParams(
result_path=path + "3/",
label_config=lc,
learn_model=True,
model={},
filename="dataset.pkl"
))
shutil.rmtree(path)
self.assertTrue(isinstance(d1, RepertoireDataset))
self.assertTrue(isinstance(d2, RepertoireDataset))
self.assertEqual(0.67, np.round(d2.encoded_data.examples[0, 2], 2))
self.assertEqual(0.0, np.round(d3.encoded_data.examples[0, 1], 2))
self.assertTrue(isinstance(encoder, KmerFrequencyEncoder))
def test_encode(self):
path = EnvironmentSettings.root_path + "test/tmp/evennessenc/"
PathBuilder.build(path)
rep1 = Repertoire.build_from_sequence_objects(sequence_objects=[
ReceptorSequence("AAA", metadata=SequenceMetadata(count=10))
for i in range(1000)
] + [
ReceptorSequence("AAA", metadata=SequenceMetadata(count=100))
for i in range(1000)
] + [
ReceptorSequence("AAA", metadata=SequenceMetadata(count=1))
for i in range(1000)
],
metadata={
"l1": "test_1",
"l2": 2
},
path=path)
rep2 = Repertoire.build_from_sequence_objects(sequence_objects=[
ReceptorSequence("AAA", metadata=SequenceMetadata(count=10))
for i in range(1000)
],
metadata={
"l1": "test_2",
"l2": 3
},
path=path)
lc = LabelConfiguration()
lc.add_label("l1", ["test_1", "test_2"])
lc.add_label("l2", [0, 3])
dataset = RepertoireDataset(repertoires=[rep1, rep2])
encoder = EvennessProfileEncoder.build_object(
dataset, **{
"min_alpha": 0,
"max_alpha": 10,
"dimension": 51
})
d1 = encoder.encode(
dataset, EncoderParams(
result_path=path + "1/",
label_config=lc,
))
encoder = EvennessProfileEncoder.build_object(
dataset, **{
"min_alpha": 0,
"max_alpha": 10,
"dimension": 11
})
d2 = encoder.encode(
dataset,
EncoderParams(result_path=path, label_config=lc, pool_size=2))
self.assertAlmostEqual(d1.encoded_data.examples[0, 0], 1)
self.assertAlmostEqual(d1.encoded_data.examples[0, 1], 0.786444)
self.assertAlmostEqual(d1.encoded_data.examples[1, 0], 1)
self.assertAlmostEqual(d1.encoded_data.examples[1, 1], 1)
shutil.rmtree(path)
